Segmentation masks INbreast

This dataset provides manually created segmentation masks of the images in the INbreast dataset by I.C. Moreira et al[1]. The masks are saved as nrrd files with pixel-wise ground truth for background (0), breast (1), and pectoral muscle (2) (when present). This dataset is created for the development of a mammogram segmentation model[2]. Segmentation masks were created in three steps, first initialization of the breast boundary by Otsu thresholding[3], second a pectoral muscle initialization for MLO images, and lastly a manual adjustment of the mask, as show in Figure 1. For the MLO views, the already publicly-available annotations of the pectoral muscle were used as the initialization. Finally, each segmentation mask was checked visually and adjusted manually using ITK-SNAP 3.6.0[4] by one of four medical imaging scientists with experience in mammography. This also includes adding pectoral muscle annotation were it was visible in CC views. [1] I. C. Moreira et al., "INbreast: Toward a Full-field Digital Mammographic Database", Acad. Radiol. 19(2), 236–248 (2012) [2] S.D. Verboom et al., "Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors", Journal of Medical Imaging, 11(1), 014001 (2023) [3] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms", IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979) [4] P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability", Neuroimage 31(3), 1116–1128 (2006)本数据集提供由I.C. Moreira等学者[1]制作的INbreast数据集图像的手动分割掩码（segmentation masks）。该掩码以nrrd文件格式存储，包含针对背景（像素值0）、乳腺组织（像素值1）及胸肌（像素值2，若图像中存在该结构）的逐像素标注真值（ground truth）。 本数据集旨在支撑乳腺钼靶图像分割模型的开发工作[2]。 分割掩码的制作分为三个步骤：首先通过大津阈值法（Otsu thresholding）[3]初始化乳腺边界；其次针对MLO视图图像进行胸肌区域初始化；最后对掩码进行手动调整，具体流程如图1所示。对于MLO视图，我们直接采用已公开的胸肌标注作为初始化依据。最终，所有分割掩码均由四位具备乳腺影像分析经验的医学影像科学家之一，使用ITK-SNAP 3.6.0[4]完成视觉检查与手动调整，其中也包括在CC视图中可见的胸肌区域添加标注。 [1] I. C. Moreira 等人, "INbreast: Toward a Full-field Digital Mammographic Database", 《学术放射学》（Acad. Radiol.）19(2), 236–248 (2012) [2] S.D. Verboom 等人, "Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors", 《医学成像期刊》（Journal of Medical Imaging）11(1), 014001 (2023) [3] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms", 《IEEE系统、人与控制论汇刊》（IEEE Trans. Syst. Man. Cybern.）9(1), 62–66 (1979) [4] P. A. Yushkevich 等人, "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability", 《神经图像》（Neuroimage）31(3), 1116–1128 (2006)